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Predictive business process monitoring methods exploit historical process execution logs to provide predictions about running instances of a process, which enable process workers and managers to preempt performance issues or compliance violations.
A number of approaches have been proposed to predict quantitative process performance indicators, suc...
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... [1], cases are replayed using a heuristics-based backtracking algorithm that searches for the best alignment between the model and a partial trace. e algorithm can be illustrated by a traversal of a process tree starting from the root node, e.g. using depth--rst search, where nodes represent partial candidate solution states (Figure 3). Here the state represents the aforementioned alignment state of the case replay. At each node, the algorithm checks whether the alignment state till that node is good enough. If so, it generates a set of child nodes of that node and continues down that path; otherwise, it stops at that node, i.e. it prunes the branch under the node, and backtracks to the parent node to traverse other branches. ...
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This paper discusses the integration of Nirdizati, a tool for predictive process monitoring, into the Web-based process analytics platform Apromore. Through this integration, Apromore’s users can use event logs stored in the Apromore repository to train a range of predictive models, and later use the trained models to predict various performance in...
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... In recent years, many researchers have applied machine learning techniques to the task of remaining time prediction, and achieved good prediction results. Verenich et al. [6] proposed a remaining time prediction method based on Long-Short Term Memory (LSTM) recurrent neural network, which beat the existing methods based on process model and machine learning and achieved the best prediction effect. Tax et al. [7] show that deep learning techniques have broad application prospects in the task of remaining time prediction. ...
... The data-driven approach applies machine learning techniques to mine remaining time prediction models directly from historical event logs. The basic approach employed in this type of work is to first use clustering techniques to divide historical process instances, each division representing a class of variants of the process, and then apply regression techniques to build remaining time prediction models on each division [6] . For machine learning methods such as clustering and regression, designing effective instance features is an important factor affecting the model effect. ...
p align="justify">Most of the existing deep learning-based business process remaining time prediction methods use traditional long-short-term memory recurrent neural networks to build prediction models. Due to the limited modeling ability of traditional long-short-term memory recurrent neural networks for sequence data, and existing methods there is still much room for improvement in the prediction effect. Aiming at the shortcomings of existing methods, this paper proposes a business process remaining time prediction method based on attention bidirectional recurrent neural network. The method uses a bidirectional recurrent neural network to model the process instance data and introduces an attention mechanism to automatically learn the weights of different events in the process instance. In addition, in order to further improve the learning effect, an iterative learning strategy is designed based on the idea of transfer learning, which builds remaining time prediction models for process instances of different lengths, which improves the pertinence of the model. The experimental results show that the proposed method has obvious advantages compared with traditional methods.</p
... In general, a transition system [2,20,21,31] or a Petri net [35] is constructed and used to predict the remaining time. Meanwhile, Verenich et al. [38] make a prediction based on the process tree obtained from historical traces. Non-process-aware approaches usually apply machine learning algorithms to learn a model from labeled training data, i.e., supervised learning [37]. ...
... Banks might have a system that decides whether clients have their loans approved. Social networks could employ an intelligent strategy to white-box models are preferred when making critical decisions that might change the course of events in a business [32,129,130], black-box models are more performant and can generalise even with high-dimensional input features in scenarios such as predicting protein relations [3,81,140,159], student dropout prediction [15,105,104,135], and trend prediction [7,70,131,148]. ...
In recent years, Graph Neural Networks have reported outstanding performance in tasks like community detection, molecule classification and link prediction. However, the black-box nature of these models prevents their application in domains like health and finance, where understanding the models' decisions is essential. Counterfactual Explanations (CE) provide these understandings through examples. Moreover, the literature on CE is flourishing with novel explanation methods which are tailored to graph learning. In this survey, we analyse the existing Graph Counterfactual Explanation methods, by providing the reader with an organisation of the literature according to a uniform formal notation for definitions, datasets, and metrics, thus, simplifying potential comparisons w.r.t to the method advantages and disadvantages. We discussed seven methods and sixteen synthetic and real datasets providing details on the possible generation strategies. We highlight the most common evaluation strategies and formalise nine of the metrics used in the literature. We first introduce the evaluation framework GRETEL and how it is possible to extend and use it while providing a further dimension of comparison encompassing reproducibility aspects. Finally, we provide a discussion on how counterfactual explanation interplays with privacy and fairness, before delving into open challenges and future works.
... A review of the literature reveals three primary predictive process monitoring approaches: model-based approaches [4,9], sequence-to-feature encoding (STEP) approaches [10,11], and simulation-based approaches [12,13]. ...
... The authors propose an approach to determine the optimal quantile for taking a point estimate from the survival curve. In [9], the authors extend that approach to predict the time-to-default for credit data sets from Belgian and UK financial institutions. ...
... # performer(en)) associated with that trace as well as the start and end event activity labels. While we adopt that approach to explore the impact of social contextual factors on process cycle time, we acknowledge that other encoding approaches, such as index-based encoding which is "lossless" [9], could also be equally adopted. Our approach is in effect a combination of aggregation and last state encoding [14] where the aggregation function computes the group degree, betweenness, closeness, and eigenvalue centrality for each trace based on the set of performers who executed the events in that trace. ...
Predictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). The quest for models with higher predictive power has led to the development of a variety of novel approaches. However, though social contextual factors are widely acknowledged to impact the way cases are handled, as yet there have been no studies which have investigated the impact of social contextual features in the predictive process monitoring framework. These factors encompass the way humans and automated agents interact within a particular organisation to execute process-related activities. This paper seeks to address this problem by investigating the impact of social contextual features in the predictive process monitoring framework utilising a survival analysis approach. We propose an approach to censor an event log and build a survival function utilising the Weibull model, which enables us to explore the impact of social contextual factors as covariates. Moreover, we propose an approach to predict the remaining time of an in-flight process instance by using the survival function to estimate the throughput time for each trace, which is then used with the elapsed time to predict the remaining time for the trace. The proposed approach is benchmarked against existing approaches using five real-life event logs and it outperforms these approaches.
... Several of these approaches are based on Annotated Transition Systems (ATS), where each (partial) trace is associated to a state [4]- [6], [14], [15]. Other approaches use a partial trace-based or index-based representation [1], [10], [16]. More recently, approaches have been proposed for applying machine learning methods for predicting the remaining time [2], [17]- [20]. ...
... In [1] authors present a prediction method based on non-Markovian Petri Nets, which are enriched with duration distributions of activities and the elapsed time since the occurrence of the last activity. In [16] authors propose a white-box approach to predict the remaining time of running process instances. The approach followed is firstly to predict the remaining time at the level of activities and then to aggregate these predictions at the level of a process instance by means of flow analysis techniques. ...
... More recent approaches use machine learning methods to predict the remaining time [11], [12], [16]. These models, in general, produce better results than the previously described approaches. ...
In this paper, we deal with one of the current challenges in process mining enhancement: the prediction of remaining times in business processes. Accurate predictions of the remaining time, defined as the required time for an instance process to finish, are critical in many systems for organisations being able to establish a priori requirements, for optimal management of resources or for improving the quality of the services organisations provide. Our approach consists of i) extracting and assessing a number of features on the business logs, that provide a structural characterisation of the traces; ii) extending the well-known annotated transition system (ATS) model to include these features; iii) proposing a partitioning strategy for the lists of features associated to each state in the extended ATS; and iv) applying a linear regression technique to each partition for predicting the remaining time of new traces. Extensive experimentation using eight attributes and ten real-life datasets show that the proposed approach outperforms in terms of mean absolute error and accuracy all the other approaches in state of the art, which includes ATS-based, non-ATS based as well as Deep Learning-based approaches.
... Furthermore, some approaches, e.g., [19] and [43], exploit contextual information, such as workload indicators, to take into account inter-case dependencies due to resource contention and data sharing. Finally, a group of works, e.g., [30] and [60] also leverage a process model in order to "replay" ongoing process cases on it. Such works treat remaining time as a cumulative indicator composed of cycle times of elementary process components. ...
... Furthermore, some process-aware approaches rely on stochastic Petri nets [41,42] and process models in BPMN notation [60]. ...
... Finally, Verenich et al. [60] propose a hybrid approach that relies on classification methods to predict routing probabilities for each decision point in a process model, regression methods to predict cycle times of future events, and flow analysis methods to calculate the total remaining time. A conceptually similar approach is proposed by Polato et al. [40] who build a transition system from an event log and enrich it with classification and regression models. ...
Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity, or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g., shifting resources from one case onto another to ensure the latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures, and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 17 real-life datasets originating from different industry domains.
... Additionally, in Bai and Sarkis (2013), the authors treat only the implementation phase of BPM life cycle for the same BP types. Then, several approaches have been proposed for the monitor phase to predict quantitative process performance metrics, such as remaining cycle time, cost, or probability of deadline violation (Leontjeva et al., 2015;Maggi et al., 2014;Metzger et al., 2012 ;Pika et al., 2012;Verenich et al., 2017) for the support and operational BPs. In ...
... Finally, a group of works, e.g. [27] and [55] also leverage a process model in order to "replay" ongoing process cases on it. Such works treat remaining time as a cumulative indicator composed of cycle times of elementary process components. ...
... Queueing theory and regression-based techniques are combined for delay prediction in [40,41]. Furthermore, some process-aware approaches rely on stochastic Petri nets [37,38] and process models in BPMN notation [55]. ...
... Finally, Verenich et al. [55] propose a hybrid approach that relies on classification methods to predict routing probabilities for each decision point in a process model, regression methods to predict cycle times of future events, and flow analysis methods to calculate the total remaining time. A conceptually similar approach is proposed by Polato et al. [36] who build a transition system from an event log and enrich it with classification and regression models. ...
Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances of a process, including predictions of the remaining cycle time of running cases of a process. A number of approaches to tackle this latter prediction problem have been proposed in the literature. However, due to differences in the experimental setups, choice of datasets, evaluation measures and baselines, the relative performance of various methods remains unclear. This article presents a systematic review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 methods based on 16 real-life datasets.
Predictive monitoring is a key activity in some Process-Aware Information Systems (PAIS) such as information systems for operational management support. Unforeseen circumstances like COVID can introduce changes in human behaviour, processes, or computing resources, which lead the owner of the process or information system to consider whether the quality of the predictions made by the system (e.g., mean time to solution) is still good enough, and if not, which amount of data and how often the system should be trained to maintain the quality of the predictions. To answer these questions, we propose, compare, and evaluate different strategies for selecting the amount of information required to update the predictive model in a context of offline learning. We performed an empirical evaluation using three real-world datasets that span between 2 and 13 years to validate the different strategies which show a significant enhancement in the prediction accuracy with respect to a non-update strategy.
